The machine learning pipeline is the process data scientists follow to build machine learning models. Oftentimes, an inefficient machine learning pipeline can hurt the data science teams’ ability to produce models at scale. Many enterprises today are focused on building a streamlined machine learning process by standardizing their workflow, and by adopting MLOps solutions.
Building an effective machine learning pipeline means you are creating a seamless process, where each step along the way flows seamlessly into the next step. It means establishing a feedback loop or continuous cycle where the models retrain using fresh data to continue producing results consistently, all while removing any friction and obstacles commonly encountered by the gap between IT, DevOps and Data science teams. But, the question is, what does it take to build the most efficient, integrated and automated process? In this post, we’ll dissect each part of the machine learning pipeline and offer strategies on how to design your machine learning pipelines.
Asking the right questions
We’re going to give it to you straight. Building an efficient pipeline all comes down to asking yourself the right questions throughout the machine learning process. At cnvrg.io we like to focus on the transition between data science and engineering. Every machine learning pipeline requires both science and engineering professionals, making it important to ask the right questions on both science and engineering tasks in order to examine ways to simplify each task. The goal is to have your data and information flow seamlessly between each stage of the ML lifecycle and optimizing any stage that can be automated. It’s important to make sure that each step has a close connection and is integrated with the next step so that you can quickly move through development and quickly get your models to production.
The desired outcome strips away the “plumbing” so that data scientists will be able to focus on their actual data science work – the algorithms, the code, and the model training.
Components of a machine learning pipeline
In order to break down between stages, we first must define the elements of a machine learning pipeline. Often, data science teams will visualize a pipeline as a straight line from end to end. It will consist of research, data processing, training, deployment, and monitoring.
In reality, a machine learning pipeline should resemble more of a cyclical and iterative process.
cnvrg.io defines the stages of the machine learning pipelines to look more like this:
Research → Data → Processing → Training → Deployment → Monitor & Retrain → back again
This full feedback loop is extremely important to building an integrated and automated process. It will also help update your machine learning models in production and learn from new data with zero downtime. Secondly, you’ll notice in the diagram above that research is fully encasing the entire pipeline. You can learn more about the feedback loop and continual machine learning pipelines in our webinar on “How to use continual learning in your ML models”.
Data science questions vs. engineering questions
The real secret to building effective machine learning pipelines is asking the right questions.
This exercise allows you to discover how your ML pipeline can be optimized. Generally speaking, there are 2 types of questions you should be asking:
Data science related questions and pipeline or “engineering” related questions.
We’ll also dissect each stage separately to examine the current process, and see how we can improve at a data science level, and at an engineering level.
Let’s get into it then. Here are the questions you should be asking yourself throughout the various stages of building your machine learning pipeline. This exercise will help you identify areas in which to improve, and help you employ specialty MLOps solutions to accelerate your machine learning pipeline from research to production and back again.
ML Pipeline questions by stage
Data Science questions:
- Where are we collecting the data? (where is our data coming from)
- Is it historical data or live data? (what type of data is it?)
- Where will it be stored?
- Streaming, batch or both? (how will we be processing the data)
- How will the data be integrated? (How will we take all the various data sources and store them in one place in order to use them?)
- API calls, NFS, HDFS, Other solutions? (In which ways will we access the data?)
- Should we and can we version our data? (You want to make the process of accessing and exploring the data as easy as possible)
Data science questions:
- What features are important? (do feature engineering)
- What shape should our data be in?
- What needs to be cleaned? (How to clean it? Get rid of null values)
- How will we automate the processing? (a manual process is a waste of time, menial tasks).
- Which compute will we use for processing? (local, cloud, on-premise – if there’s a security issue?)
- Should we use a distributed compute, such as Spark? (this enables us to quickly analyze large volumes of data)
- How can we easily leverage compute for this step?
Data science questions:
- What model will we use? (Deep learning or classic models? Is the complexity of deep learning useful in this case or will it be less efficient?)
- Will we be doing HPO? (This is a complicated process, requiring many retrainings and iterations of the training).
- How will we compare models?
- What is our accuracy metric?
- How will we parallelize HPO? (Otherwise you will need to wait for results)
- Which kind of compute will we use for training? (Local, cloud, on-premise? CPU, GPU)
- How will we manage artifacts and models? (You’ll need a system to collect them, catalogue them and compare them easily).
- How can we automate the comparison of experiments? (Getting caught up in the nitty-gritty tech work is actually wasteful, and takes a lot of time. You want something efficient).
Amazingly, 65% of models never make it to production. Here are some questions to help you beat the statistics. Reasons for this may vary, some examples are miscommunication between teams internally, or a difficulty in engineering a solution for deployment, to name a few. To deploy successfully, you need an enterprise level solution which will allow you to build a model, put it into production and get it functioning as a service as quickly as possible.
Data science questions:
- Where are we deploying to? (Network, edge?)
- Batch or streaming? (what kind of querying are we going to do of that end point?)
- Can we autoscale?
- How can we quickly deploy our best model? (How do we get this done now?)
- Can this be automated? (Can we automatically put this into production?)
- Should this be automated? (maybe you won’t be able to identify which is the best model)
- How can we track input and output?
- Do we need a human in the loop? (Depends on a lot of different factors)
Monitor and Retrain
Data science questions:
- What are we monitoring? Inputs, outputs? (Logs, usage, demand?)
- Can we detect model accuracy while it’s live in production?
- How can we automatically trigger retraining? (Set up a system that can trigger retraining).
- How do we redeploy with zero downtime? (Keeping your models in production with no downtime).
- Can we take input data and export it to dataset? (you need a system that captures your data and joins it into your datasets).
Choosing the right tools
There are many tools that claim to help streamline your pipeline, but often these are incomplete solutions, consisting of different, disconnected pieces. It can be difficult to connect all these tools, and often will make your pipeline even further siloed and disconnected. Ideally, you’d like a system that captures the inputs and joins them into your datasets, which creates the feedback loop. Some tools may require deep technical knowledge to even set up, which may prove to be counterproductive. It is important to make sure to research your tools thoroughly, otherwise, you may find yourself sinking time into setting up a tool that will prove to be unhelpful. The tool should be flexible and able to connect to other components that you use in the process. Think about your team’s needs and make sure to adopt solutions that align with those needs. Things you might want to consider are tools that are language agnostic, leverages your existing resources, and has the ability to leverage the many AI frameworks your team uses.
valuating your machine learning pipeline
In order to succeed in building your machine learning pipeline, it’s important to ask the right questions throughout the process. Planning is pivotal to accelerating your machine learning pipeline and workflow. Find tools that will save you time and simplify the workflow. With advanced MLOps solutions, your team should be able to eliminate grunt work, and create a seamless workflow connecting science and engineering. Make sure to link each stage of your pipeline to the next for maximum flow, and consider those stages as a continuous feedback loop. The more seamless and efficient the pipeline, the quicker you can build and develop models. Good luck!
Interested in seeing more about it? Watch our latest webinar about building ML pipelines.